RBF network based adaptive sliding mode control for solar sails

被引:11
|
作者
Lian, Xiaobin [1 ]
Liu, Jiafu [1 ]
Wang, Chuang [1 ]
Yuan, Tiger [1 ]
Cui, Naigang [2 ]
机构
[1] Shenyang Aerosp Univ, Shenyang, Liaoning, Peoples R China
[2] Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China
来源
关键词
Finite-time stability; Control vanes; RBF; Sliding mode saturation control; Solar sails; ATTITUDE-CONTROL SYSTEM; ORBITS; DESIGN;
D O I
10.1108/AEAT-04-2017-0112
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Purpose The purpose of this paper is to resolve complex nonlinear dynamical problems of the pitching axis of solar sail in body coordinate system compared with inertial coordinate system. And saturation condition of controlled torque of vane in the orbit with big eccentricity ration, uncertainty and external disturbance under complex space background are considered. Design/methodology/approach The pitch dynamics of the sailcraft in the prescribed elliptic earth orbits is established considering the torques by the control vanes, gravity gradient and offset between the center-of-mass (cm) and center-of-pressure (cp). The maximal torques afforded by the control vanes are numerically determined for the sailcraft at any position with any pitch angle, which will be used as the restriction of the attitude control torques. The finite/infinite time adaptive sliding mode saturation controller and Bang-Bang-Radial Basis Function (RBF) controller are designed for the sailcraft with restricted attitude control torques. The model uncertainty and the input error (the error between real input and ideal control law input) are solved using the RBF network. Findings The finite true anomaly adaptive sliding mode saturation controller performed better than the other two controllers by comparing the numerical results in the paper. The control torque saturation, the model uncertainty and the external disturbance were also effectively solved using the infinite and finite time adaptive sliding mode saturation controllers by analyzing the numerical simulations. The stabilization of the pitch motion was accomplished within half orbit period. Practical implications The complex accurate dynamics can be approximated using the RBF network. The controllers can be applied to stabilization of spacecraft attitude dynamics with uncertainties in complex space environment. Originality/value Advanced control method is used in this paper; saturation of controlled torque of vane is resolved when the orbit with big eccentricity ration is considered and uncertainty and external disturbance under complex space background are settled. Moreover, complex and accurate nonlinear dynamical model of pitching axis of solar sail in body coordinate system compared with inertial coordinate system is provided.
引用
收藏
页码:1180 / 1191
页数:12
相关论文
共 50 条
  • [1] Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor
    Alqaisi, Walid Kh.
    Brahmi, Brahim
    Ghommam, Jawhar
    Saad, Maarouf
    Nerguizian, Vahe
    2019 IEEE INTERNATIONAL SYMPOSIUM ON ROBOTIC AND SENSORS ENVIRONMENTS (ROSE 2019), 2019, : 77 - 83
  • [2] RBF Neural Network based Adaptive Sliding Mode Control for Hypersonic Flight Vehicles
    Wang, Jianmin
    Wang, Jinbo
    Zhang, Tao
    2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), 2016, : 58 - 63
  • [3] Adaptive backstepping and sliding mode control of fin stabilizer based on RBF neural network
    Zhang, Yuantao
    Shi, Weiren
    Yin, Lingling
    Qiu, Mingbai
    Zhao, Lin
    2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 2, 2009, : 302 - +
  • [4] RBF Neural Network Adaptive Sliding Mode Control Based on Genetic Algorithm Optimization
    Zhao Jie
    Han Long
    Ren Sijing
    PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 6772 - 6775
  • [5] Backstepping Sliding Mode RBF Network Adaptive Control for Quadrotor UAV
    Shen, Weihao
    Li, Zhong
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 4086 - 4091
  • [6] Adaptive RBF Neural Network Control Based on Sliding Mode Controller for Active Power Filter
    Fei Juntao
    Wang Zhe
    Lu Xiaochun
    Deng Lihua
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 3288 - 3293
  • [7] Research on Adaptive Sliding Mode Robust Control Algorithm of Manipulator Based on RBF Neural Network
    Tian, Hua
    Liang, Yanbing
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 4625 - 4629
  • [8] Adaptive sliding mode control of manipulator based on RBF network minimum parameter learning method
    Cui, Yongfeng
    Tian, Chong
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2016, 19 (01): : 185 - 197
  • [9] RBF Network Adaptive Sliding Mode Control of Ball and Plate System Based on Reaching Law
    Li, Jiang-Feng
    Xiang, Feng-Hong
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (08) : 9393 - 9404
  • [10] RBF Network Adaptive Sliding Mode Control of Ball and Plate System Based on Reaching Law
    Jiang-Feng Li
    Feng-Hong Xiang
    Arabian Journal for Science and Engineering, 2022, 47 : 9393 - 9404